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Assessment of temporal probability for rainfall-induced landslides based on nonstationary extreme value analysis
Abstract Climate change may alter the frequency and intensity of rainfall, and thereby significantly affect the frequency and magnitude of shallow landslides. To accurately evaluate the temporal probability of landslide initiation, it is therefore important to consider the effect of climate variation. Although various approaches have been proposed to estimate the temporal probability of landslides to date, most of them were based on the stationary assumption, i.e., that the statistical properties of the historical rainfall data are time-invariant. However, if historical rainfall data show nonstationary characteristics such as a trend or an abrupt change, the stationary assumption is no longer valid and induces a miscalculation. In this study, we propose a new approach that can estimate the temporal probability of future rainfall-induced landslide occurrence while incorporating the nonstationary characteristics of the rainfall data. In assessing such data, a nonstationary generalized extreme value distribution was used to evaluate the temporal probability. Then, by combining the derived nonstationary temporal probability with landslide susceptibility results obtained from the random forest model, probabilities of landslide occurrence were calculated for future periods, from 1 to 50 years, and compared with the results based on a stationary model. The results showed that the stationary model underestimated the landslide probability compared with the nonstationary approach. This is because an increasing trend in local rainfall, taken from a gauge in the study area, was not considered in the stationary analysis. Thus, climate change has ongoing consequences for landslide occurrence. To reflect the impacts of climate change, a nonstationary approach capable of coping with climate variation should therefore be considered in any landslide hazard analysis.
Highlights A process for evaluating landslide probability considering nonstationarity is developed. Multiple statistical tests are used to assess nonstationarity in extreme rainfalls. Nonstationary EVA is used to estimate temporal probability of landslide occurrence. Landslide probability is estimated by combining temporal probability with susceptibility. Future landslide probability from nonstationary model is greater than from stationary model.
Assessment of temporal probability for rainfall-induced landslides based on nonstationary extreme value analysis
Abstract Climate change may alter the frequency and intensity of rainfall, and thereby significantly affect the frequency and magnitude of shallow landslides. To accurately evaluate the temporal probability of landslide initiation, it is therefore important to consider the effect of climate variation. Although various approaches have been proposed to estimate the temporal probability of landslides to date, most of them were based on the stationary assumption, i.e., that the statistical properties of the historical rainfall data are time-invariant. However, if historical rainfall data show nonstationary characteristics such as a trend or an abrupt change, the stationary assumption is no longer valid and induces a miscalculation. In this study, we propose a new approach that can estimate the temporal probability of future rainfall-induced landslide occurrence while incorporating the nonstationary characteristics of the rainfall data. In assessing such data, a nonstationary generalized extreme value distribution was used to evaluate the temporal probability. Then, by combining the derived nonstationary temporal probability with landslide susceptibility results obtained from the random forest model, probabilities of landslide occurrence were calculated for future periods, from 1 to 50 years, and compared with the results based on a stationary model. The results showed that the stationary model underestimated the landslide probability compared with the nonstationary approach. This is because an increasing trend in local rainfall, taken from a gauge in the study area, was not considered in the stationary analysis. Thus, climate change has ongoing consequences for landslide occurrence. To reflect the impacts of climate change, a nonstationary approach capable of coping with climate variation should therefore be considered in any landslide hazard analysis.
Highlights A process for evaluating landslide probability considering nonstationarity is developed. Multiple statistical tests are used to assess nonstationarity in extreme rainfalls. Nonstationary EVA is used to estimate temporal probability of landslide occurrence. Landslide probability is estimated by combining temporal probability with susceptibility. Future landslide probability from nonstationary model is greater than from stationary model.
Assessment of temporal probability for rainfall-induced landslides based on nonstationary extreme value analysis
Kim, Hanbeen (author) / Lee, Jung-Hyun (author) / Park, Hyuck-Jin (author) / Heo, Jun-Haeng (author)
Engineering Geology ; 294
2021-09-07
Article (Journal)
Electronic Resource
English
Temporal probability , Nonstationary GEV distribution , Rainfall-induced landslides , Random forest , Probability of landslide occurrence , AIC , Akaike information criterion , AM , annual maximum , AUC , area under the curve , CDF , cumulative distribution function , EV , extreme value , EVA , extreme value analysis , GEV , generalized extreme value , KMA , Korea Meteorological Administration , ML , maximum likelihood , NIDP , National Institute for Disaster Prevention , NS-EVA , nonstationary extreme value analysis , NS-GEV , nonstationary generalized extreme value , OOB , out-of-bag , PDF , probability density function , RCP , representative concentration pathway , RF , random forest , ROC , receiver operating characteristics , SPI , stream power index , TIN , triangulated irregular network , TWI , topographic wetness index
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